New renewable energy is attracting considerable attention as a future energy source. The photovoltaic (PV) market, in particular, has grown significantly during the past decade. The use of the rooftop PV system in buildings in urban environments is being actively promoted. This research was conducted to develop a framework for the analysis of the potential of the rooftop PV system to achieve the net-zero energy solar buildings in terms of energy supply. To verify the feasibility of the proposed framework, a total of 5418 elementary school facilities located in 16 administrative divisions in South Korea were selected as case studies. This research (i) collected information on the elementary school facilities, the rooftop PV system, and the meteorological and geographical characteristics by region; (ii) conducted an energy supply analysis by applying the rooftop PV system; (iii) conducted an energy demand analysis; (iv) analyzed the energy substitution effect; (v) presented the current status of the energy supply and demand in each region using the geographical information system; (vi) analyzed the causal relationship between the energy supply and demand by region; and (vii) proposed an energy supply and demand strategy by region. This research can help elementary school facility managers or policymakers conduct an energy supply and demand analysis as well as propose an energy supply and demand strategy. It can be used as part of an educational facility improvement program. The framework proposed in this research can also be applied to any other country or sector in the global environment.
PurposeThe purpose of this study was to investigate and propose the appropriate K‐mapping models as an approach to integrating key project components and technologies for the effective improvement of project performance within and across construction projects.Design/methodology/approachIn this holistic, single‐case study, one of the largest construction consulting firms in South Korea has been studied by conducting 15 semi‐structured interviews and the different loci for each of the K‐mapping components are identified and analyzed. Based on the different loci, four types of the K‐mapping model are provided and elucidated.FindingsResearch findings indicate that these four types of the K‐mapping model provide the criteria to identify the appropriate types of K‐map for construction project organizations, according to the characteristics and conditions of their own construction personnel, construction processes, and K‐transfer technologies. With the K‐mapping models, an appropriate knowledge management system (KMS) can be developed more effectively.Research limitations/implicationsFirst, as interpretivism was adopted as the research philosophy, the case study findings were subjective and qualitative to both the interviewees in the case study company and the researchers, though this study provided an important underpinning for future research on K‐mapping within construction project organizations. Second, the theory developed in this study was based on an investigation of the appropriate K‐mapping models with only a single case study. Nevertheless, this case study provided sufficient data and information to develop and propose a theory for successful K‐mapping model development within construction project organizations.Originality/valueIn the KM area, the definition, benefits, purposes, principles and types of K‐map have been already provided by many KM researchers and practitioners. However, no industry (practical)‐based K‐mapping model has been developed and proposed, especially in the construction industry. Accordingly, the originality of this study to be presented in one of the paper's conclusions: construction processes must be considered and adopted as a key component in the K‐mapping process, and the discussion of the four types of K‐map this research have generated, which significantly expands the existing literature on K‐mapping.
In digital pathology, pathological tissue images that are obtained using scanners are analyzed and diseases are diagnosed. One crucial aspect of this process is the staining of the tissue slides. However, differences appear in the staining color even when using the same staining protocol owing to various factors such as different facilities, hospitals, and scanning equipment. Many stain style normalization studies have been conducted to solve this problem. In this study, we propose a model named multi-domain single image reconstruction-based stain-style transfer. The proposed model is trained using a reconstruction-based learning framework, which can efficiently reduce the complexity and training time compared with that associated with the GAN objective. We randomly extracted stained tissue image patches from the CAME-LYON17 and Mitos-Atypia-14 datasets and demonstrated an effective stain-style translation. Our study reveals that it is possible to perform translation among multiple domains using a single training image per domain. Furthermore, we experimentally demonstrated that translation among color temperature domains was possible in the natural image domain. Our code is publicly available at: https://github.com/jwkweon/ MS-SST.
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